How fast does biological rock crust grow?

set.seed(15102020)
bootstraps <- 1000
data_path <- "./Data/"
Proj_name <- "BRC_growth_rate"

Browns <- RColorBrewer::brewer.pal(n = 9, "YlOrBr")[9:6]
Greens <- RColorBrewer::brewer.pal(n = 9, "YlGn")[9:6]
Blues <- RColorBrewer::brewer.pal(n = 9, "YlGnBu")[9:6]
Gradient.colours <- c(Browns[1], Greens[1], Browns[2], Greens[2], Browns[3], Greens[3], Browns[4], Greens[4], Blues)

OTUmat <- t(read.csv(paste0(data_path, "Shivta_site_otuTab2.txt"), header = TRUE, row.names = 1))
sort.order <- as.numeric(gsub("OTU([0-9]+)", "\\1", colnames( OTUmat )))
OTUmat <- OTUmat[, order(sort.order )]

Metadata <- read.csv(paste0(data_path, "Shivta_metadata.csv"), row.names = 1, header = TRUE)

read_csv(paste0(data_path, "Shivta_metadata.csv"),
                     trim_ws = TRUE) %>%
  mutate_at(
    c(
      "Rock.type",
      "Location"
    ), 
    ~(factor(.))
  ) %>% 
  column_to_rownames("Sample.code") ->
  Metadata

row.names(OTUmat) <- gsub("(.*)Nimrod[0-9]+|Osnat[0-9]+", "\\1", row.names( OTUmat))
Metadata <- Metadata[order(row.names(Metadata)), ]
OTUmat <- OTUmat[order(row.names(OTUmat)), ]
# calculate sample size
Metadata$Library.size = rowSums(OTUmat)
Metadata$Location.rock <- with(Metadata, Location:Rock.type)

# Load taxonomy data
tax.file <- "Shivta_site_silva.nrv119.taxonomy"
Taxonomy <- read.table(paste0(data_path, tax.file), stringsAsFactors = FALSE) # read taxonomy file

# count how many ';' in each cell and add up to 6
for (i in 1:nrow(Taxonomy)){
  semicolons <- length(gregexpr(";", Taxonomy$V2[i] )[[1]])
  if (semicolons < 6){
    x <- paste0( rep("Unclassified;", 6 - semicolons ), collapse = "")
    Taxonomy$V2[i] <- paste0( Taxonomy$V2[i], x, sep = "")
  }
}
# split taxonomy to columns
do.call( "rbind", strsplit( Taxonomy$V1, ";", fixed = TRUE)) %>% 
  gsub( "size=([0-9]+)", "\\1", .) %>%
  data.frame( ., do.call( "rbind", strsplit( Taxonomy$V2, ";", fixed = TRUE)), stringsAsFactors = F) %>% 
  apply(., 2, function(x) gsub( "\\(.*\\)", "", x)) %>% 
  replace(., . == "unclassified", "Unclassified") -> 
  Taxonomy
colnames( Taxonomy ) <- c( "OTU", "Frequency", "Domain", "Phylum", "Class", "Order", "Family", "Genus" )
# rownames(Taxonomy) <- colnames(Rock_weathering_OTUmat)
rownames(Taxonomy) <- Taxonomy[, 1]

Tree_IQ <- read_tree(paste0(data_path, "Shivta_site_otuReps.filtered.align.treefile"))

# generate phyloseq object
Ps_obj <- phyloseq(otu_table(OTUmat, taxa_are_rows = FALSE),
                   tax_table(Taxonomy[, -c(1, 2)]),
                   sample_data(Metadata),
                   phy_tree(Tree_IQ)
)
# Reorder factors for plotting
sample_data(Ps_obj)$Location %<>% fct_relevel("Slope", "City")

domains2remove <- c("", "Archaea", "Eukaryota", "Unclassified")
classes2remove <- c("Chloroplast")
families2remove <- c("Mitochondria")
Ps_obj_filt <- subset_taxa(Ps_obj, !is.na(Phylum) &
                        !Domain %in% domains2remove &
                      !Class %in% classes2remove &
                      !Family %in% families2remove)

Ps_obj_df <-
  as.data.frame(sample_data(Ps_obj_filt)) # Put sample_data into a ggplot-friendly data.frame
Ps_obj_df <- Ps_obj_df[order(Ps_obj_df$Library.size), ]
Ps_obj_df$Index <- seq(nrow(Ps_obj_df))
ggplot(data = Ps_obj_df, 
       aes(x = Index, y = Library.size, color = Location.rock)) + 
  geom_point(size = 4) + 
  scale_colour_manual(values = ggpomological:::pomological_palette[c(2, 1, 9, 3)], name = "Location.rock")

summary(sample_sums(Ps_obj_filt))

summary(taxa_sums(Ps_obj_filt))

prevdf <- apply(X = otu_table(Ps_obj_filt),
                 MARGIN = ifelse(taxa_are_rows(Ps_obj_filt), yes = 1, no = 2),
                 FUN = function(x){sum(x > 0)})
# Add taxonomy and total read counts to this data.frame
prevdf <- data.frame(Prevalence = prevdf,
                      TotalAbundance = taxa_sums(Ps_obj_filt),
                      tax_table(Ps_obj_filt))
prevdf %>%
  group_by(Phylum) %>%
  summarise(`Mean prevalence` = mean(Prevalence),
            `Sum prevalence` = sum(Prevalence)) ->
  Prevalence_phylum_summary

Prevalence_phylum_summary %>% 
  kable(., digits = c(0, 1, 0)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)

prevdf %>%
  group_by(Order) %>%
  summarise(`Mean prevalence` = mean(Prevalence),
            `Sum prevalence` = sum(Prevalence)) ->
  Prevalence_Order_summary

Prevalence_Order_summary %>% 
  kable(., digits = c(0, 1, 0)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)

Prevalence_phylum_summary %>% 
  filter(`Sum prevalence` < 8) %>% 
  dplyr::select(Phylum) %>% 
  map(as.character) %>% 
  unlist() ->
  filterPhyla
Ps_obj_filt %<>% subset_taxa(!Phylum %in% filterPhyla)
sample_data(Ps_obj_filt)$Library.size <- rowSums(otu_table(Ps_obj_filt))
print(Ps_obj)

print(Ps_obj_filt)

# Subset to the remaining phyla
prevdf_phylum_filt <- subset(prevdf, Phylum %in% get_taxa_unique(Ps_obj_filt, "Phylum"))
ggplot(prevdf_phylum_filt,
       aes(TotalAbundance, Prevalence / nsamples(Ps_obj_filt), color = Phylum)) +
  # Include a guess for parameter
  geom_hline(yintercept = 0.05,
             alpha = 0.5,
             linetype = 2) + geom_point(size = 2, alpha = 0.7) +
  scale_x_log10() +  xlab("Total Abundance") + ylab("Prevalence [Frac. Samples]") +
  facet_wrap( ~ Phylum) + theme(legend.position = "none")

# Subset to the remaining phyla
prevdf_order_filt <- subset(prevdf, Order %in% get_taxa_unique(Ps_obj_filt, "Order"))
# grab the top 30 most abundant orders
prevdf_order_filt %>% 
  group_by(Order) %>%
  summarise(Combined.abundance = sum(TotalAbundance)) %>% 
  arrange(desc(Combined.abundance)) %>% 
  .[1:30, "Order"]  ->
  Orders2plot
prevdf_order_filt2 <- subset(prevdf, Order %in% Orders2plot$Order)
ggplot(prevdf_order_filt2,
       aes(TotalAbundance, Prevalence / nsamples(Ps_obj_filt), color = Order)) +
  # Include a guess for parameter
  geom_hline(yintercept = 0.05,
             alpha = 0.5,
             linetype = 2) + geom_point(size = 2, alpha = 0.7) +
  scale_x_log10() +  xlab("Total Abundance") + ylab("Prevalence [Frac. Samples]") +
  facet_wrap( ~ Order) + theme(legend.position = "none")

# Define prevalence threshold as 10% of total samples
prevalenceThreshold <- 0.1 * nsamples(Ps_obj_filt)
prevalenceThreshold

# Execute prevalence filter, using `prune_taxa()` function
keepTaxa <-
  row.names(prevdf_phylum_filt)[(prevdf_phylum_filt$Prevalence >= prevalenceThreshold)]
Ps_obj_filt  %<>%  prune_taxa(keepTaxa, .)
sample_data(Ps_obj_filt)$Library.size <- rowSums(otu_table(Ps_obj_filt))
print(Ps_obj)

print(Ps_obj_filt)

PlotLibDist(Ps_obj_filt)

sample_data(Ps_obj_filt) %>% 
  as_tibble() %>% 
  dplyr::select(Sample.name, Library.size) %>% 
  as(., "data.frame") %>% 
  kable(.) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)

(mod1 <- adonis(
  otu_table(Ps_obj_filt) ~ Library.size,
  data = as(sample_data(Ps_obj_filt), "data.frame"), 
  method = "horn",
  permutations = 9999
))

PlotReadHist(as(otu_table(Ps_obj_filt), "matrix"))

notAllZero <- (rowSums(t(otu_table(Ps_obj_filt))) > 0)
vsn::meanSdPlot(as.matrix(log2(t(otu_table(Ps_obj_filt))[notAllZero, ] + 1)))

Ps_obj_filt_GMPR <- Ps_obj_filt
Ps_obj_filt %>%
  otu_table(.) %>%
  t() %>%
  as(., "matrix") %>%
  GMPR() ->
  GMPR_factors

Ps_obj_filt %>%
  otu_table(.) %>%
  t() %*% diag(1 / GMPR_factors$gmpr) %>%
  t() %>%
  as.data.frame(., row.names = sample_names(Ps_obj_filt)) %>%
  otu_table(., taxa_are_rows = FALSE) ->
  otu_table(Ps_obj_filt_GMPR)
sample_data(Ps_obj_filt_GMPR)$Library.size <- sample_sums(Ps_obj_filt)
adonis(
  otu_table(Ps_obj_filt_GMPR) ~ Library.size,
  data = as(sample_data(Ps_obj_filt_GMPR), "data.frame"),
  method = "horn",
  permutations = 9999
)

PlotLibDist(Ps_obj_filt_GMPR)

PlotReadHist(as(otu_table(Ps_obj_filt_GMPR), "matrix"))

notAllZero <- (rowSums(t(otu_table(Ps_obj_filt_GMPR))) > 0)
vsn::meanSdPlot(as.matrix(log2(t(otu_table(Ps_obj_filt_GMPR))[notAllZero, ] + 1)))

rarefaction.mat <- matrix(0, nrow = nsamples(Ps_obj_filt), ncol = bootstraps)
rownames(rarefaction.mat) <- sample_names(Ps_obj_filt)
rich.ests <- list(S.obs = rarefaction.mat, S.chao1 = rarefaction.mat, se.chao1 = rarefaction.mat,
                   S.ACE = rarefaction.mat, se.ACE = rarefaction.mat)
for (i in seq(bootstraps)) {
  sub.OTUmat <- rrarefy(otu_table(Ps_obj_filt), min(rowSums(otu_table(Ps_obj_filt))))
  for (j in seq(length(rich.ests))) {
    rich.ests[[j]][, i] <- t(estimateR(sub.OTUmat))[, j]
  }
}
Richness <- data.frame(row.names = row.names(rich.ests[[1]]))
for (i in c(1, seq(2, length(rich.ests), 2))) {
  S <- apply(rich.ests[[i]], 1, mean)
  if (i == 1) { 
    se <- apply(rich.ests[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
    } else se <- apply(rich.ests[[i + 1]], 1, mean)
  Richness <- cbind(Richness, S, se)
}
colnames(Richness) <- c("S.obs", "S.obs.se", "S.chao1", "S.chao1.se", "S.ACE", "S.ACE.se")
saveRDS(Richness, file = paste0("./Results/", Proj_name, "_richness.RDS"))
write.csv(Richness, file = paste0("./Results/", Proj_name, "_richness.csv"))
ses <- grep("\\.se", colnames(Richness))
Richness[, ses] %>% 
  gather(key = "est.se") -> se.dat
Richness[, -unique(ses)] %>% 
  gather(key = "est") -> mean.dat
n <- length(unique(mean.dat$est))
# diversity indices
diversity.inds <- list(Shannon = rarefaction.mat, inv.simpson = rarefaction.mat, BP = rarefaction.mat)
for (i in seq(bootstraps)) {
  sub.OTUmat <- rrarefy(otu_table(Ps_obj_filt), min(rowSums(otu_table(Ps_obj_filt))))
  diversity.inds$Shannon[, i] <- diversityresult(sub.OTUmat, index = 'Shannon', method = 'each site', digits = 3)[, 1]
  diversity.inds$inv.simpson[, i] <- diversityresult(sub.OTUmat, index = 'inverseSimpson', method = 'each site', digits = 3)[, 1]
  diversity.inds$BP[, i] <- diversityresult(sub.OTUmat, index = 'Berger', method = 'each site', digits = 3)[, 1]
}
Diversity <- data.frame(row.names = row.names(diversity.inds[[1]]))
for (i in seq(length(diversity.inds))) {
  S <- apply(diversity.inds[[i]], 1, mean)
  se <- apply(diversity.inds[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
  Diversity <- cbind(Diversity, S, se)
}
colnames(Diversity) <- c("Shannon", "Shannon.se", "Inv.simpson", "Inv.simpson.se", "BP", "BP.se")
ses <- grep("\\.se", colnames(Diversity))
Diversity[, ses] %>% gather(key = "est.se") -> se.dat
Diversity[, -unique(ses)] %>% gather(key = "est") -> mean.dat
saveRDS(Diversity, file = paste0("./Results/", Proj_name, "_diversity.RDS"))
write.csv(Diversity, file = paste0("./Results/", Proj_name, "_diversity.csv"))

# make combined richness diversity
Richness_Diversity <- cbind(Richness, Diversity)
ses <- grep("\\.se", colnames(Richness_Diversity))
Richness_Diversity[, ses] %>% 
  gather(key = "est.se") -> 
  se.dat
Richness_Diversity[, -unique(ses)] %>% 
  gather(key = "Metric", 
         value = "Estimate") -> 
  mean.dat
Richness_Diversity_long <-
  cbind(
    Sample = rep(rownames(Richness_Diversity), times = length(unique(mean.dat$Metric))),
    mean.dat,
    lerr = mean.dat$Estimate - se.dat$value,
    herr = mean.dat$Estimate + se.dat$value
  )
Richness_Diversity_long$Metric <-
  factor(
    Richness_Diversity_long$Metric,
    levels = c("S.obs", "S.chao1", "S.ACE", "Shannon", "Inv.simpson", "BP"),
    labels = c("S obs.", "Chao1", "ACE", "Shannon", "Inv. Simpson" , "Berger Parker")
  )
Richness_Diversity_long %<>%
  cbind(., 
        sample_data(Ps_obj_filt))

# S Obs
data2test <- Richness_Diversity_long[Richness_Diversity_long$Metric == "S obs.", ] 

(mod_obsS <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "S obs.")))

# Post-hoc test
marginal <- emmeans(mod_obsS,
                   ~ Location : Rock.type)
summary(marginal)

contrast(marginal, 
         method = "pairwise", 
         adjust = "tukey")

(obsS_pairwise <- cld(marginal,
                      alpha = 0.05,
                      Letters = letters,
                      adjust = "tukey")) # works with lm but not with two-factor ART

(mod_obsS %>% 
  anova() %>% 
  mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
  mod_obsS_ANOVA)

# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_obsS, Location ~ Rock.type)

# summary(as.glht(pairs(marginal))) # fails because of unbalanced design

# Shannon
(mod_Shannon <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "Shannon")))

# Post-hoc test
marginal <- emmeans(mod_Shannon,
                   ~ Location : Rock.type)
summary(marginal)

contrast(marginal, 
         method = "pairwise", 
         adjust = "tukey")

(Shannon_pairwise <- cld(marginal,
                      alpha = 0.05,
                      Letters = letters,
                      adjust = "tukey")) # works with lm but not with two-factor ART

(mod_Shannon %>% 
  anova() %>% 
  mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
  mod_Shannon_ANOVA)

# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_Shannon, Location ~ Rock.type)

# ACE
(mod_ACE <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "ACE")))

# Post-hoc test
marginal <- emmeans(mod_ACE,
                   ~ Location : Rock.type)
summary(marginal)

contrast(marginal, 
         method = "pairwise", 
         adjust = "tukey")

(ACE_pairwise <- cld(marginal,
                      alpha = 0.05,
                      Letters = letters,
                      adjust = "tukey")) # works with lm but not with two-factor ART

(mod_ACE %>% 
  anova() %>% 
  mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
  mod_ACE_ANOVA)

# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_ACE, Location ~ Rock.type)

# summary(as.glht(pairs(marginal))) # fails because of unbalanced design

#Inv. Simpson
(mod_InvSim <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "Inv. Simpson")))

# Post-hoc test
marginal <- emmeans(mod_InvSim,
                   ~ Location : Rock.type)
summary(marginal)

contrast(marginal, 
         method = "pairwise", 
         adjust = "tukey")

(InvSim_pairwise <- cld(marginal,
                      alpha = 0.05,
                      Letters = letters,
                      adjust = "tukey")) # works with lm but not with two-factor ART

(mod_InvSim %>% 
  anova() %>% 
  mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
  mod_InvSim_ANOVA)

# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_InvSim, Location ~ Rock.type)

# summary(as.glht(pairs(marginal))) # fails because of unbalanced design


#Berger Parker
(mod_BP <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "Berger Parker")))

# Post-hoc test
marginal <- emmeans(mod_BP,
                   ~ Location : Rock.type)
summary(marginal)

contrast(marginal, 
         method = "pairwise", 
         adjust = "tukey")

(BP_pairwise <- cld(marginal,
                      alpha = 0.05,
                      Letters = letters,
                      adjust = "tukey")) # works with lm but not with two-factor ART

(mod_BP %>% 
  anova() %>% 
  mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
  mod_BP_ANOVA)

# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_BP, Location ~ Rock.type)

# summary(as.glht(pairs(marginal))) # fails because of unbalanced design

Richness_Diversity_long %>% 
  dplyr::filter(!Metric %in% c("Chao1", "ACE")) %>% 
    mutate_at(., "Metric", ~fct_recode(., "Observed S" = "S obs.", "Inv. Simpson" = "Inv. Simpson", "Berger Parker" = "Berger Parker")) %>% 
  mutate_at(., "Metric", ~fct_relevel(., "Observed S", "Inv. Simpson", "Shannon", "Berger Parker")) %>% 
  droplevels() ->
  Richness_Diversity_long2plot

p_alpha <- ggplot() +
  geom_violin(data = Richness_Diversity_long2plot,
             aes(
               x = Location,
               y = Estimate,
               ymin = lerr,
               ymax = herr
             ), colour = "grey",
              fill = "grey",
              alpha = 1 / 3) +
  geom_jitter2(data = Richness_Diversity_long2plot,
               aes(x = Location,
               y = Estimate,
               ymin = lerr,
               ymax = herr,
               colour = Location), size = 3, width = 0.2, alpha = 2/3) +
  scale_colour_manual(values = Gradient.colours[c(5, 6, 11)], name = "") +
  # geom_errorbar(alpha = 1 / 2, width = 0.3) +
  xlab("") +
  ylab("") +
  theme(axis.text.x = element_text(
    angle = 45,
    vjust = 0.9,
    hjust = 1
  ),
  legend.position="none") +
  facet_grid(Metric ~ Rock.type, scale = "free") +
  background_grid(major = "y",
                  minor = "none",
                  size.major = 0.8) 

dat_text <- data.frame(
  label = str_remove_all(c(obsS_pairwise$.group[1:4], 
                           Shannon_pairwise$.group[1:4], 
                           InvSim_pairwise$.group[1:4],
                           BP_pairwise$.group[1:4]), 
                         pattern = " "),
  Metric = fct_inorder(rep(levels(Richness_Diversity_long2plot$Metric), each = 4)),
  Rock.type = fct_c(obsS_pairwise$Rock.type[1:4], 
                           Shannon_pairwise$Rock.type[1:4], 
                           InvSim_pairwise$Rock.type[1:4],
                           BP_pairwise$Rock.type[1:4]), 
  x = fct_c(obsS_pairwise$Location[1:4], 
                           Shannon_pairwise$Location[1:4], 
                           InvSim_pairwise$Location[1:4],
                           BP_pairwise$Location[1:4]),
  # x     = as.factor(levels(Richness_Diversity_long2plot$Climate.Source)),
  y = rep(c(520, 45, 5.2, 0.52), each = 4)
  # y = rep(c(40, 140, 0.5), each = 6)
)
p_alpha <- p_alpha + geom_text(
  data = dat_text,
  mapping = aes(x = x, y = y, label = label),
  nudge_x = 0,
  nudge_y = 0
)
print(p_alpha)

(mod1 <-  adonis(
  otu_table(Ps_obj_filt_GMPR) ~ Location * Rock.type,
  data = as(sample_data(Ps_obj_filt_GMPR), "data.frame"),
  method = "horn",
  permutations = 9999
))

(mod2 <- adonis(
  otu_table(Ps_obj_filt_GMPR) ~ Location,
  data = as(sample_data(Ps_obj_filt_GMPR), "data.frame"),
  method = "horn",
  permutations = 9999
))

mod1_pairwise <- PairwiseAdonis(
  otu_table(Ps_obj_filt_GMPR),
  sample_data(Ps_obj_filt_GMPR)$Location,
  sim.function = "vegdist",
  sim.method = "horn",
  p.adjust.m = "BH"
)
print(mod1_pairwise)

(sig_pairs1 <- list(as.character(mod1_pairwise$pairs[mod1_pairwise$p.adjusted < 0.05])))

mod2_pairwise <- PairwiseAdonis(
  otu_table(Ps_obj_filt_GMPR),
  sample_data(Ps_obj_filt_GMPR)$Rock.type,
  sim.function = "vegdist",
  sim.method = "horn",
  p.adjust.m = "BH"
)
print(mod2_pairwise)

(sig_pairs2 <- list(as.character(mod2_pairwise$pairs[mod2_pairwise$p.adjusted < 0.05])))

mod3_pairwise <- PairwiseAdonis(
  otu_table(Ps_obj_filt_GMPR),
  sample_data(Ps_obj_filt_GMPR)$Location.rock,
  sim.function = "vegdist",
  sim.method = "horn",
  p.adjust.m = "BH"
)
print(mod3_pairwise)

(sig_pairs3 <- list(as.character(mod3_pairwise$pairs[mod3_pairwise$p.adjusted < 0.1])))

Unifrac_mat <- UniFrac(Ps_obj_filt, 
                       weighted = TRUE, 
                       normalized = TRUE, 
                       parallel = TRUE, 
                       fast = TRUE)

(mod1 <-  adonis(
  Unifrac_mat ~ Location * Rock.type,
  data = as(sample_data(Ps_obj_filt_GMPR), "data.frame"),
  method = "horn",
  permutations = 9999
))

(mod2 <- adonis(
  Unifrac_mat ~ Location,
  data = as(sample_data(Ps_obj_filt_GMPR), "data.frame"),
  method = "horn",
  permutations = 9999
))

Ps_obj_ord1 <- ordinate(Ps_obj_filt_GMPR, "CAP", "horn", formula = Ps_obj_filt_GMPR ~  Location * Rock.type)
Ps_obj_ord2 <- ordinate(Ps_obj_filt_GMPR, "CAP", "horn", formula = Ps_obj_filt_GMPR ~  Location)

explained <- eigenvals(Ps_obj_ord2)/sum( eigenvals(Ps_obj_ord2)) * 100
explained <- as.numeric(format(round(explained, 1), nsmall = 1))

Ps_obj_filt_GMPR %>% 
  plot_ordination(., Ps_obj_ord2, type = "samples", shape = "Rock.type", color = "Location", justDF = TRUE) -> 
  ord_df

p_ord <- ggplot(ord_df,
             aes(
               x = CAP1,
               y = MDS1,
               shape = Rock.type,
               color = Location
             )) +
  stat_ellipse(
    aes(x = CAP1, 
        y = MDS1, 
        fill = Location
        ),
    geom = "polygon",
    alpha = 1/4,
    type = "t",
    level = 0.9,
    # linetype = 2,
    inherit.aes = FALSE
  ) +
  geom_point(size = 4, alpha = 2 / 3) +
  guides(colour = guide_legend(title = "Location"), shape = guide_legend(title = "Rock.type")) +
  scale_colour_manual(values = Gradient.colours) +
  scale_fill_manual(values = Gradient.colours, guide = "none") +
  labs(x = sprintf("CAP1 (%s%%)", explained[1]), 
       y = sprintf("CAP2 (%s%%)", explained[2])) +
  coord_fixed(ratio = sqrt(explained[2] / explained[1])) +
   theme(legend.justification = "top")
  # facet_wrap(. ~ Rock.type)
print(p_ord)

Ps_obj_ord1 <- ordinate(Ps_obj_filt, "PCoA", "Unifrac", formula = Ps_obj_filt ~ Location * Rock.type)
Ps_obj_ord2 <- ordinate(Ps_obj_filt, "PCoA", "Unifrac", formula = Ps_obj_filt ~ Location)

explained <- Ps_obj_ord2$values$Relative_eig/sum(Ps_obj_ord2$values$Relative_eig) * 100
explained <- as.numeric(format(round(explained, 1), nsmall = 1))

Ps_obj_filt %>% 
  plot_ordination(., Ps_obj_ord2, type = "samples", shape = "Rock.type", color = "Location", justDF = TRUE) -> 
  ord_df

p_ord_phylo <- ggplot(ord_df,
             aes(
               x = Axis.1,
               y = Axis.2,
               shape = Rock.type,
               color = Location
             )) +
  stat_ellipse(
    aes(x = Axis.1, 
        y = Axis.2, 
        fill = Location
        ),
    geom = "polygon",
    alpha = 1/4,
    type = "t",
    level = 0.9,
    # linetype = 2,
    inherit.aes = FALSE
  ) +
  geom_point(size = 4, alpha = 2 / 3) +
  theme_bw(base_size = 14) +
  guides(colour = guide_legend(title = "Location"), shape = guide_legend(title = "Rock.type")) +
  scale_colour_manual(values = Gradient.colours) +
  scale_fill_manual(values = Gradient.colours, guide = "none") +
  labs(x = sprintf("CAP1 (%s%%)", explained[1]), 
       y = sprintf("CAP2 (%s%%)", explained[2])) +
  coord_fixed(ratio = sqrt(explained[2] / explained[1])) #+ 
  # facet_wrap(. ~ Rock.type)
print(p_ord_phylo)

Taxa_tests_phylum1 <- STAMPR2(Ps_obj_filt, vars2test = "Location", threshold = 0.05, outputfile = paste0(Proj_name, "_Location"))

pSTAMPR1 <- plotSTAMPR(Taxa_tests_phylum1, pair = "Slope - City")
print(pSTAMPR1)

Taxa_tests_phylum2 <- STAMPR2(Ps_obj_filt, vars2test = c("Location", "Rock.type"), threshold = 0.05, outputfile = paste0(Proj_name, "_Location_Rock"))

pSTAMPR2 <- plotSTAMPR(Taxa_tests_phylum2, pair = "Slope:Chalk - City:Chalk")
print(pSTAMPR2)

Ps_obj_filt_GMPR %>% 
  transform_sample_counts(., function(x){x / sum(x)} * 100) %>% 
  tax_glom(., 
           "Phylum",
           NArm = TRUE) ->
  Ps_obj_filt_GMPR_glom

Ps_obj_filt_GMPR_glom_DF <- speedyseq::psmelt(Ps_obj_filt_GMPR_glom)
Ps_obj_filt_GMPR_glom_DF$Phylum %<>% as.character()
# Ps_obj_filt3_glom_DF %<>% mutate(Species = fct_relevel(Species, "NA", after = Inf))

# group dataframe by Phylum, calculate sum rel. abundance
Ps_obj_filt_GMPR_glom_DF %>%
  group_by(Phylum) %>%
  summarise(Sum = sum(Abundance) / nsamples(Ps_obj_filt_GMPR_glom) ) ->
  Sums

# find Phyla whose rel. abund. is less than 5%
Rare_phyla0.05 <- Sums[Sums$Sum <= 0.05, ]$Phylum

# change their name to "Rare"
Ps_obj_filt_GMPR_glom_DF[Ps_obj_filt_GMPR_glom_DF$Phylum %in% Rare_phyla0.05, ]$Phylum <- 'Rare'
# re-group
Ps_obj_filt_GMPR_glom_DF %>%
  group_by(Sample, Phylum, Location, Rock.type, Location.rock) %>%
  summarise(Abundance = sum(Abundance)) ->
  Ps_obj_filt_GMPR_glom_DF_2plot

# ab.taxonomy$Freq <- sqrt(ab.taxonomy$Freq)
# Ps_obj_filt3_glom_rel_DF$Phylum %<>% sub("unclassified", "Unclassified", .)
# Ps_obj_filt3_glom_rel_DF$Phylum %<>% sub("uncultured", "Unclassified", .)

Ps_obj_filt_GMPR_glom_DF_2plot %>% 
  group_by(Sample) %>% 
  filter(Phylum == "Rare") %>% 
  summarise(`Rares (%)` = sum(Abundance)) -> 
  Rares

# Percentage of reads classified as rare 
Rares %>%
  kable(., digits = c(2), caption = "Percentage of reads per sample classified as rare:") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)

sample_order <- match(Rares$Sample, row.names(sample_data(Ps_obj_filt_GMPR_glom)))
Rares %<>% arrange(., sample_order)

Rares %>% 
  cbind(., sample_data(Ps_obj_filt_GMPR_glom)) %>% 
  group_by(Location.rock) %>% 
  setNames(make.names(names(.), unique = TRUE)) %>% # fails for some reason without it
  summarise(`Rares (%)` = mean(`Rares....`)) -> 
  Rares_merged

# Percentage of reads classified as rare 
Rares_merged %>%
  kable(., digits = c(2), caption = "Percentage of reads per sample classified as rare:") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)

Ps_obj_filt_GMPR_glom_DF_2plot %>% 
  group_by(Phylum) %>% 
  summarise(sum.Taxa = sum(Abundance)) %>% 
  arrange(desc(sum.Taxa)) -> Taxa_rank
Ps_obj_filt_GMPR_glom_DF_2plot$Phylum %<>% 
  factor(., levels = Taxa_rank$Phylum) %>% 
  fct_relevel(., "Rare", after = Inf)
  
p_taxa_box <-
  ggplot(Ps_obj_filt_GMPR_glom_DF_2plot, aes(x = Phylum, y = (Abundance))) +
  geom_boxplot(aes(group = interaction(Phylum, Location)), position = position_dodge(width = 0.9), fatten = 1) +
  geom_point(
    aes(colour = Rock.type),
    position = position_jitterdodge(dodge.width = 1),
    alpha = 1 / 2,
    stroke = 0,
    size = 2
  ) +
  scale_colour_manual(values = Gradient.colours, name = "") +
  theme_bw()+
  # theme_cowplot(font_size = 11, font_family = f_name) +
  labs(x = NULL, y = "Relative abundance (%)") +
  guides(colour = guide_legend(override.aes = list(size = 5))) +
  facet_grid(Location ~ .) +
  background_grid(major = "xy",
                  minor = "none") +
  theme(axis.text.x = element_text(
    angle = 45,
    vjust = 0.9,
    hjust = 0.9
  ),
  legend.position = c(.99, .99),
  legend.justification = c("right", "top"),
  legend.box.just = "top",
  legend.margin = margin(0, 3, 3, 3))
print(p_taxa_box)

Ps_obj_filt_GMPR_glom <- tax_glom(Ps_obj_filt_GMPR, 
                             "Phylum", 
                             NArm = TRUE) # glomerate to the phylum level
Ps_obj_filt_GMPR_glom_rel <- transform_sample_counts(Ps_obj_filt_GMPR_glom, function(x) x / sum(x)) # transform to rel. ab.
Ps_obj_filt_GMPR_glom_rel_DF <- speedyseq::psmelt(Ps_obj_filt_GMPR_glom_rel) # generate a df
Ps_obj_filt_GMPR_glom_rel_DF$Phylum %<>% as.character() # factor to char

# group dataframe by Phylum, calculate sum rel. abundance
Ps_obj_filt_GMPR_glom_rel_DF %>%
  group_by(Phylum) %>%
  summarise(Sum = sum(Abundance) / nsamples(Ps_obj_filt_GMPR_glom) ) ->
  Sums

# find Phyla whose mean rel. abund. is less than 0.5%
Rare_phyla0.05 <- Sums[Sums$Sum <= 0.05, ]$Phylum

# change their name to "Rare"
Ps_obj_filt_GMPR_glom_rel_DF[Ps_obj_filt_GMPR_glom_rel_DF$Phylum %in% Rare_phyla0.05, "Phylum"] <- 'Rare' 

# re-group
Ps_obj_filt_GMPR_glom_rel_DF %>%
  group_by(Phylum) %>%
  summarise(Abundance = sum(Abundance)) %>% 
  arrange(desc(Abundance)) -> Taxa_rank

comparison_string <- c("City", "Slope")

Ps_obj_filt %>%
  subset_samples(Location %in% c(comparison_string[1], comparison_string[2])) %>%
  tax_glom("Order") ->
  Ps_obj_filt_pairwise_glom

# Test differential abundance for location
da_Loc <- differentialTest(formula = ~ Location,
                           phi.formula = ~ Location,
                           formula_null = ~ 1,
                           phi.formula_null = ~ Location, 
                           test = "Wald", boot = FALSE,
                           data = Ps_obj_filt,
                           fdr_cutoff = 0.05,
                           full_output = TRUE)
da_Loc_intervals <- plot(da_Loc, level = "Class", data_only = T)
which(is.na(da_Loc$p)) %>% names

Ps_obj_filt %>%
  transform_sample_counts(., function(x) x / sum(x) * 100) %>% 
  taxa_sums(.) %>% 
  map_dbl(~(.x / nsamples(Ps_obj_filt))) %>% 
  enframe(name = "OTU", value = "Mean abundance (%)") -> 
  baseMean

map(da_Loc$all_models,15) %>% 
  map(.,2) %>% 
  unlist %>%  # grab all mu.LocationSlope Estimates (differences in estimated population relative abundance)
  bind_cols(OTU = taxa_names(Ps_obj_filt), 
            tax_table(Ps_obj_filt), 
            `Differential abundance` = .,
            Significance = fct_recode(as_factor(taxa_names(Ps_obj_filt) %in% da_Loc$significant_taxa), Pass = "TRUE", Fail = "FALSE"),
            ymin = as.numeric(NA),
            ymax = as.numeric(NA)
            ) %>%
  left_join(., baseMean, by = "OTU") ->
  da_Loc_df

da_Loc_df %<>% rows_update(., tibble(ymin = da_Loc_intervals$xmin, OTU = da_Loc$significant_taxa), by = "OTU")
da_Loc_df %<>% rows_update(., tibble(ymax = da_Loc_intervals$xmax, OTU = da_Loc$significant_taxa), by = "OTU")
da_Loc_df[da_Loc_df$Phylum %in% Rare_phyla0.05, "Phylum"] <- 'Rare' # rare_phyla is

p_corncob_loc <- GGPlotCorncob(da_Loc_df, OTU_labels = FALSE, Taxa = "Phylum", Y_val = "Differential abundance", sig_level = 0.05, Rank = Taxa_rank)

corncob_summary <- tibble(Label = c(paste0("⬆", sum(da_Loc_df$`Differential abundance` > 0 &  da_Loc_df$Significance == "Pass"), " ⬇", sum(da_Loc_df$`Differential abundance` < 0 &  da_Loc_df$Significance == "Pass"), " (", nrow(da_Loc_df), ")")))

p_corncob_loc <- p_corncob_loc +
  labs(title = paste(comparison_string, collapse = " - ")) +
  coord_cartesian(ylim = c(-15, 15))
print(p_corncob_loc)

comparison_string <- c("Limestone", "Chalk")
# Test differential abundance and variance for rock type
da_Rock <- differentialTest(formula = ~ Rock.type,
                                 phi.formula = ~ Rock.type,
                                 formula_null = ~ 1,
                                 phi.formula_null = ~ Rock.type, 
                                 test = "Wald", boot = FALSE,
                                 data = Ps_obj_filt,
                                 fdr_cutoff = 0.05,
                                full_output = TRUE)
da_Rock_intervals <- plot(da_Rock, level = "Class", data_only = TRUE)
which(is.na(da_Rock$p)) %>% names

map(da_Rock$all_models,15) %>% 
  map(.,2) %>% 
  unlist %>%  # grab all mu.LocationSlope Estimates (differences in estimated population relative abundance)
  bind_cols(OTU = taxa_names(Ps_obj_filt), 
            tax_table(Ps_obj_filt), 
            `Differential abundance` = .,
            Significance = fct_recode(as_factor(taxa_names(Ps_obj_filt) %in% da_Rock$significant_taxa), Pass = "TRUE", Fail = "FALSE"),
            ymin = as.numeric(NA),
            ymax = as.numeric(NA)
            ) %>%
  left_join(., baseMean, by = "OTU") ->
  da_Rock_df

da_Rock_df %<>% rows_update(., tibble(ymin = da_Rock_intervals$xmin, OTU = da_Rock$significant_taxa), by = "OTU")
da_Rock_df %<>% rows_update(., tibble(ymax = da_Rock_intervals$xmax, OTU = da_Rock$significant_taxa), by = "OTU")
da_Rock_df[da_Rock_df$Phylum %in% Rare_phyla0.05, "Phylum"] <- 'Rare' # rare_phyla is

p_corncob_rock <- GGPlotCorncob(da_Rock_df, OTU_labels = FALSE, Taxa = "Phylum", Y_val = "Differential abundance", sig_level = 0.05, Rank = Taxa_rank)

corncob_summary <- tibble(Label = c(paste0("⬆", sum(da_Rock_df$`Differential abundance` > 0 &  da_Rock_df$Significance == "Pass"), " ⬇", sum(da_Rock_df$`Differential abundance` < 0 &  da_Rock_df$Significance == "Pass"), " (", nrow(da_Rock_df), ")")))

p_corncob_rock <- p_corncob_rock +
  labs(title = paste(comparison_string, collapse = " - ")) +
  coord_cartesian(ylim = c(-15, 15))
print(p_corncob_rock)

# Test differential abundance for location, control for Rock.type for both cases
comparison_string <- c("City", "Slope")
da_Loc_exRock <- differentialTest(formula = ~ Location + Rock.type,
                                 phi.formula = ~ Location + Rock.type,
                                 formula_null = ~ Rock.type,
                                 phi.formula_null = ~ Location + Rock.type, 
                                 test = "Wald", boot = FALSE,
                                 data = Ps_obj_filt,
                                 fdr_cutoff = 0.05,
                                full_output = TRUE)
da_Loc_exRock_intervals <- plot(da_Loc_exRock, level = "Class", data_only = TRUE)

which(is.na(da_Loc_exRock$p)) %>% names

map(da_Loc_exRock$all_models, 15) %>% 
  map(., 2) %>% 
  unlist %>%  # grab all mu.LocationSlope Estimates (differences in estimated population relative abundance)
  bind_cols(OTU = taxa_names(Ps_obj_filt), 
            tax_table(Ps_obj_filt), 
            `Differential abundance` = .,
            Significance = fct_recode(as_factor(taxa_names(Ps_obj_filt) %in% da_Loc_exRock$significant_taxa), Pass = "TRUE", Fail = "FALSE"),
            ymin = as.numeric(NA),
            ymax = as.numeric(NA)
            ) %>%
  left_join(., baseMean, by = "OTU") ->
  da_Loc_exRock_df

da_Loc_exRock_df %<>% rows_update(., tibble(ymin = da_Loc_exRock_intervals$xmin, OTU = da_Loc_exRock$significant_taxa), by = "OTU")
da_Loc_exRock_df %<>% rows_update(., tibble(ymax = da_Loc_exRock_intervals$xmax, OTU = da_Loc_exRock$significant_taxa), by = "OTU")
da_Loc_exRock_df[da_Loc_exRock_df$Phylum %in% Rare_phyla0.05, "Phylum"] <- 'Rare' # rare_phyla is

p_corncob_locExroc <- GGPlotCorncob(da_Loc_exRock_df, OTU_labels = FALSE, Taxa = "Phylum", Y_val = "Differential abundance", sig_level = 0.05, Rank = Taxa_rank)

corncob_summary <- tibble(Label = c(paste0("⬆", sum(da_Loc_exRock_df$`Differential abundance` > 0 &  da_Loc_exRock_df$Significance == "Pass"), " ⬇", sum(da_Loc_exRock_df$`Differential abundance` < 0 &  da_Loc_exRock_df$Significance == "Pass"), " (", nrow(da_Loc_exRock_df), ")")))

p_corncob_locExroc <- p_corncob_locExroc +
  labs(title = paste(comparison_string, collapse = " - ")) +
  coord_cartesian(ylim = c(-15, 15))
print(p_corncob_locExroc)

mod260 <- bbdml(formula = OTU260 ~ 1,
             phi.formula = ~ 1,
             data = Ps_obj_filt)
mod260_Loc <- bbdml(formula = OTU260 ~ Location,
             phi.formula = ~ Location,
             data = Ps_obj_filt)
mod260_Loc_rock <- bbdml(formula = OTU97 ~ Location*Rock.type,
             phi.formula = ~ Location*Rock.type,
             data = Ps_obj_filt)
lrtest(mod_null = mod260, mod = mod260_Loc)

# lrtest(mod_null = mod260_Loc, mod = mod260_Loc_rock)
summary(mod260_Loc)

plot(mod260_Loc, color = "Location", shape = "Rock.type") # add total = TRUE for total counts (i.e. not relative abundance)

composite_plot <- ((p_alpha + p_taxa_box +  plot_layout(widths = c(1, 2))) /(p_ord + pSTAMPR1) + plot_annotation(tag_levels = 'A') & theme(plot.tag = element_text(size = f_size)))

plot_file <- "./Results/Microbiome_1"
svglite(paste0(plot_file, ".svg"), 
        width = 14, 
        height = 10.5)
print(composite_plot)
dev.off()

ggsave(
  paste0(plot_file, ".png"),
  composite_plot,
  device = agg_png,
  width = 14, 
  height = 10.5, 
  units = "cm", 
  res = 900,
  scaling = 0.38
)
gz(paste0(plot_file, ".svg"), paste0(plot_file, ".svgz"))
knitr::include_graphics(paste0(plot_file, ".png"))

plot_file <- "./Results/Microbiome_2"
svglite(paste0(plot_file, ".svg"), 
        width = 12, 
        height = 10)
print(p_corncob_locExroc)
dev.off()

ggsave(
  paste0(plot_file, ".png"),
  p_corncob_locExroc,
  device = agg_png,
  width = 12, 
  height = 10, 
  units = "cm", 
  res = 900,
  scaling = 0.38
)
gz(paste0(plot_file, ".svg"), paste0(plot_file, ".svgz"))